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With Python 3 being the future of Python while Python 2 is still in active
use, it is good to have your project available for both major releases of
Python. This guide is meant to help you figure out how best to support both
Python 2 & 3 simultaneously.

A key point about supporting Python 2 & 3 simultaneously is that you can start
today! Even if your dependencies are not supporting Python 3 yet that does
not mean you can’t modernize your code now to support Python 3. Most changes
required to support Python 3 lead to cleaner code using newer practices even in
Python 2 code.

Another key point is that modernizing your Python 2 code to also support
Python 3 is largely automated for you. While you might have to make some API
decisions thanks to Python 3 clarifying text data versus binary data, the
lower-level work is now mostly done for you and thus can at least benefit from
the automated changes immediately.

Keep those key points in mind while you read on about the details of porting
your code to support Python 2 & 3 simultaneously.

While you can make Python 2.5 work with Python 3, it is much easier if you
only have to work with Python 2.7. If dropping Python 2.5 is not an
option then the six project can help you support Python 2.5 & 3 simultaneously
(pipinstallsix). Do realize, though, that nearly all the projects listed
in this HOWTO will not be available to you.

If you are able to skip Python 2.5 and older, then the required changes
to your code should continue to look and feel like idiomatic Python code. At
worst you will have to use a function instead of a method in some instances or
have to import a function instead of using a built-in one, but otherwise the
overall transformation should not feel foreign to you.

But you should aim for only supporting Python 2.7. Python 2.6 is no longer
freely supported and thus is not receiving bugfixes. This means you will have
to work around any issues you come across with Python 2.6. There are also some
tools mentioned in this HOWTO which do not support Python 2.6 (e.g., Pylint),
and this will become more commonplace as time goes on. It will simply be easier
for you if you only support the versions of Python that you have to support.

Make sure you specify the proper version support in your setup.py file¶

In your setup.py file you should have the proper trove classifier
specifying what versions of Python you support. As your project does not support
Python 3 yet you should at least have
ProgrammingLanguage::Python::2::Only specified. Ideally you should
also specify each major/minor version of Python that you do support, e.g.
ProgrammingLanguage::Python::2.7.

Once you have your code supporting the oldest version of Python 2 you want it
to, you will want to make sure your test suite has good coverage. A good rule of
thumb is that if you want to be confident enough in your test suite that any
failures that appear after having tools rewrite your code are actual bugs in the
tools and not in your code. If you want a number to aim for, try to get over 80%
coverage (and don’t feel bad if you find it hard to get better than 90%
coverage). If you don’t already have a tool to measure test coverage then
coverage.py is recommended.

Once you have your code well-tested you are ready to begin porting your code to
Python 3! But to fully understand how your code is going to change and what
you want to look out for while you code, you will want to learn what changes
Python 3 makes in terms of Python 2. Typically the two best ways of doing that
is reading the “What’s New” doc for each release of Python 3 and the
Porting to Python 3 book (which is free online). There is also a handy
cheat sheet from the Python-Future project.

Once you feel like you know what is different in Python 3 compared to Python 2,
it’s time to update your code! You have a choice between two tools in porting
your code automatically: Futurize and Modernize. Which tool you choose will
depend on how much like Python 3 you want your code to be. Futurize does its
best to make Python 3 idioms and practices exist in Python 2, e.g. backporting
the bytes type from Python 3 so that you have semantic parity between the
major versions of Python. Modernize,
on the other hand, is more conservative and targets a Python 2/3 subset of
Python, directly relying on six to help provide compatibility. As Python 3 is
the future, it might be best to consider Futurize to begin adjusting to any new
practices that Python 3 introduces which you are not accustomed to yet.

Regardless of which tool you choose, they will update your code to run under
Python 3 while staying compatible with the version of Python 2 you started with.
Depending on how conservative you want to be, you may want to run the tool over
your test suite first and visually inspect the diff to make sure the
transformation is accurate. After you have transformed your test suite and
verified that all the tests still pass as expected, then you can transform your
application code knowing that any tests which fail is a translation failure.

Unfortunately the tools can’t automate everything to make your code work under
Python 3 and so there are a handful of things you will need to update manually
to get full Python 3 support (which of these steps are necessary vary between
the tools). Read the documentation for the tool you choose to use to see what it
fixes by default and what it can do optionally to know what will (not) be fixed
for you and what you may have to fix on your own (e.g. using io.open() over
the built-in open() function is off by default in Modernize). Luckily,
though, there are only a couple of things to watch out for which can be
considered large issues that may be hard to debug if not watched for.

In Python 3, 5/2==2.5 and not 2; all division between int values
result in a float. This change has actually been planned since Python 2.2
which was released in 2002. Since then users have been encouraged to add
from__future__importdivision to any and all files which use the / and
// operators or to be running the interpreter with the -Q flag. If you
have not been doing this then you will need to go through your code and do two
things:

Add from__future__importdivision to your files

Update any division operator as necessary to either use // to use floor
division or continue using / and expect a float

The reason that / isn’t simply translated to // automatically is that if
an object defines a __truediv__ method but not __floordiv__ then your
code would begin to fail (e.g. a user-defined class that uses / to
signify some operation but not // for the same thing or at all).

In Python 2 you could use the str type for both text and binary data.
Unfortunately this confluence of two different concepts could lead to brittle
code which sometimes worked for either kind of data, sometimes not. It also
could lead to confusing APIs if people didn’t explicitly state that something
that accepted str accepted either text or binary data instead of one
specific type. This complicated the situation especially for anyone supporting
multiple languages as APIs wouldn’t bother explicitly supporting unicode
when they claimed text data support.

To make the distinction between text and binary data clearer and more
pronounced, Python 3 did what most languages created in the age of the internet
have done and made text and binary data distinct types that cannot blindly be
mixed together (Python predates widespread access to the internet). For any code
that deals only with text or only binary data, this separation doesn’t pose an
issue. But for code that has to deal with both, it does mean you might have to
now care about when you are using text compared to binary data, which is why
this cannot be entirely automated.

To start, you will need to decide which APIs take text and which take binary
(it is highly recommended you don’t design APIs that can take both due to
the difficulty of keeping the code working; as stated earlier it is difficult to
do well). In Python 2 this means making sure the APIs that take text can work
with unicode and those that work with binary data work with the
bytes type from Python 3 (which is a subset of str in Python 2 and acts
as an alias for bytes type in Python 2). Usually the biggest issue is
realizing which methods exist on which types in Python 2 & 3 simultaneously
(for text that’s unicode in Python 2 and str in Python 3, for binary
that’s str/bytes in Python 2 and bytes in Python 3). The following
table lists the unique methods of each data type across Python 2 & 3
(e.g., the decode() method is usable on the equivalent binary data type in
either Python 2 or 3, but it can’t be used by the textual data type consistently
between Python 2 and 3 because str in Python 3 doesn’t have the method). Do
note that as of Python 3.5 the __mod__ method was added to the bytes type.

Text data

Binary data

decode

encode

format

isdecimal

isnumeric

Making the distinction easier to handle can be accomplished by encoding and
decoding between binary data and text at the edge of your code. This means that
when you receive text in binary data, you should immediately decode it. And if
your code needs to send text as binary data then encode it as late as possible.
This allows your code to work with only text internally and thus eliminates
having to keep track of what type of data you are working with.

The next issue is making sure you know whether the string literals in your code
represent text or binary data. You should add a b prefix to any
literal that presents binary data. For text you should add a u prefix to
the text literal. (there is a __future__ import to force all unspecified
literals to be Unicode, but usage has shown it isn’t as effective as adding a
b or u prefix to all literals explicitly)

As part of this dichotomy you also need to be careful about opening files.
Unless you have been working on Windows, there is a chance you have not always
bothered to add the b mode when opening a binary file (e.g., rb for
binary reading). Under Python 3, binary files and text files are clearly
distinct and mutually incompatible; see the io module for details.
Therefore, you must make a decision of whether a file will be used for
binary access (allowing binary data to be read and/or written) or textual access
(allowing text data to be read and/or written). You should also use io.open()
for opening files instead of the built-in open() function as the io
module is consistent from Python 2 to 3 while the built-in open() function
is not (in Python 3 it’s actually io.open()). Do not bother with the
outdated practice of using codecs.open() as that’s only necessary for
keeping compatibility with Python 2.5.

The constructors of both str and bytes have different semantics for the
same arguments between Python 2 & 3. Passing an integer to bytes in Python 2
will give you the string representation of the integer: bytes(3)=='3'.
But in Python 3, an integer argument to bytes will give you a bytes object
as long as the integer specified, filled with null bytes:
bytes(3)==b'\x00\x00\x00'. A similar worry is necessary when passing a
bytes object to str. In Python 2 you just get the bytes object back:
str(b'3')==b'3'. But in Python 3 you get the string representation of the
bytes object: str(b'3')=="b'3'".

Finally, the indexing of binary data requires careful handling (slicing does
not require any special handling). In Python 2,
b'123'[1]==b'2' while in Python 3 b'123'[1]==50. Because binary data
is simply a collection of binary numbers, Python 3 returns the integer value for
the byte you index on. But in Python 2 because bytes==str, indexing
returns a one-item slice of bytes. The six project has a function
named six.indexbytes() which will return an integer like in Python 3:
six.indexbytes(b'123',1).

To summarize:

Decide which of your APIs take text and which take binary data

Make sure that your code that works with text also works with unicode and
code for binary data works with bytes in Python 2 (see the table above
for what methods you cannot use for each type)

Mark all binary literals with a b prefix, textual literals with a u
prefix

Decode binary data to text as soon as possible, encode text as binary data as
late as possible

Open files using io.open() and make sure to specify the b mode when
appropriate

Inevitably you will have code that has to choose what to do based on what
version of Python is running. The best way to do this is with feature detection
of whether the version of Python you’re running under supports what you need.
If for some reason that doesn’t work then you should make the version check be
against Python 2 and not Python 3. To help explain this, let’s look at an
example.

Let’s pretend that you need access to a feature of importlib that
is available in Python’s standard library since Python 3.3 and available for
Python 2 through importlib2 on PyPI. You might be tempted to write code to
access e.g. the importlib.abc module by doing the following:

The problem with this code is what happens when Python 4 comes out? It would
be better to treat Python 2 as the exceptional case instead of Python 3 and
assume that future Python versions will be more compatible with Python 3 than
Python 2:

The best solution, though, is to do no version detection at all and instead rely
on feature detection. That avoids any potential issues of getting the version
detection wrong and helps keep you future-compatible:

Once you have fully translated your code to be compatible with Python 3, you
will want to make sure your code doesn’t regress and stop working under
Python 3. This is especially true if you have a dependency which is blocking you
from actually running under Python 3 at the moment.

To help with staying compatible, any new modules you create should have
at least the following block of code at the top of it:

You can also run Python 2 with the -3 flag to be warned about various
compatibility issues your code triggers during execution. If you turn warnings
into errors with -Werror then you can make sure that you don’t accidentally
miss a warning.

You can also use the Pylint project and its --py3k flag to lint your code
to receive warnings when your code begins to deviate from Python 3
compatibility. This also prevents you from having to run Modernize or Futurize
over your code regularly to catch compatibility regressions. This does require
you only support Python 2.7 and Python 3.4 or newer as that is Pylint’s
minimum Python version support.

After you have made your code compatible with Python 3 you should begin to
care about whether your dependencies have also been ported. The caniusepython3
project was created to help you determine which projects
– directly or indirectly – are blocking you from supporting Python 3. There
is both a command-line tool as well as a web interface at
https://caniusepython3.com.

The project also provides code which you can integrate into your test suite so
that you will have a failing test when you no longer have dependencies blocking
you from using Python 3. This allows you to avoid having to manually check your
dependencies and to be notified quickly when you can start running on Python 3.

Once your code works under Python 3, you should update the classifiers in
your setup.py to contain ProgrammingLanguage::Python::3 and to not
specify sole Python 2 support. This will tell anyone using your code that you
support Python 2 and 3. Ideally you will also want to add classifiers for
each major/minor version of Python you now support.

Once you are able to fully run under Python 3 you will want to make sure your
code always works under both Python 2 & 3. Probably the best tool for running
your tests under multiple Python interpreters is tox. You can then integrate
tox with your continuous integration system so that you never accidentally break
Python 2 or 3 support.

You may also want to use the -bb flag with the Python 3 interpreter to
trigger an exception when you are comparing bytes to strings or bytes to an int
(the latter is available starting in Python 3.5). By default type-differing
comparisons simply return False, but if you made a mistake in your
separation of text/binary data handling or indexing on bytes you wouldn’t easily
find the mistake. This flag will raise an exception when these kinds of
comparisons occur, making the mistake much easier to track down.

And that’s mostly it! At this point your code base is compatible with both
Python 2 and 3 simultaneously. Your testing will also be set up so that you
don’t accidentally break Python 2 or 3 compatibility regardless of which version
you typically run your tests under while developing.

Another way to help port your code is to use a static type checker like
mypy or pytype on your code. These tools can be used to analyze your code as
if it’s being run under Python 2, then you can run the tool a second time as if
your code is running under Python 3. By running a static type checker twice like
this you can discover if you’re e.g. misusing binary data type in one version
of Python compared to another. If you add optional type hints to your code you
can also explicitly state whether your APIs use textual or binary data, helping
to make sure everything functions as expected in both versions of Python.